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Coral Reef Fish Detection and Recognition in Underwater Videos by Supervised Machine Learning: Comparison Between Deep Learning and HOG+SVM Methods

  • Sébastien VillonEmail author
  • Marc Chaumont
  • Gérard Subsol
  • Sébastien Villéger
  • Thomas Claverie
  • David Mouillot
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10016)

Abstract

In this paper, we present two supervised machine learning methods to automatically detect and recognize coral reef fishes in underwater HD videos. The first method relies on a traditional two-step approach: extraction of HOG features and use of a SVM classifier. The second method is based on Deep Learning. We compare the results of the two methods on real data and discuss their strengths and weaknesses.

Keywords

Support Vector Machine Feature Vector Coral Reef Deep Learn Convolutional Neural Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgement

This work has been carried out thanks to the support of the LabEx NUMEV project (no ANR-10-LABX-20) funded by the “Investissements d’Avenir” French Government program, managed by the French National Research Agency (ANR). We thank very much Jérôme Pasquet and Lionel Pibre for scientific discussions.

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Sébastien Villon
    • 1
    Email author
  • Marc Chaumont
    • 1
    • 2
  • Gérard Subsol
    • 2
  • Sébastien Villéger
    • 3
  • Thomas Claverie
    • 3
  • David Mouillot
    • 3
  1. 1.LIRMMUniversity of Montpellier/CNRSMontpellierFrance
  2. 2.University of NîmesNîmesFrance
  3. 3.MARBECIRD/Ifremer/University of Montpellier/CNRSMontpellierFrance

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